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Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-06
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country.
In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting.
The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems.
Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm optimization algorithm and cultural algorithm).
Taking China’s annual energy consumption as the empirical example, the effectiveness of this proposed PSOCA-GRNN model was proved.
The calculation result shows that this proposed hybrid model outperforms the single GRNN model, GRNN model optimized by PSO (PSO-GRNN), discrete grey model (DGM (1, 1)), and ordinary least squares linear regression (OLS_LR) model.
American Psychological Association (APA)
Zhao, Huiru& Guo, Sen. 2014. Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1033613
Modern Language Association (MLA)
Zhao, Huiru& Guo, Sen. Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model. Abstract and Applied Analysis No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1033613
American Medical Association (AMA)
Zhao, Huiru& Guo, Sen. Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1033613
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033613